1 code implementation • 6 May 2022 • Khushdeep Singh Mann, Abhishek Tomy, Anshul Paigwar, Alessandro Renzaglia, Christian Laugier
Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation.
1 code implementation • ICRA 2022 • Abhishek Tomy, Anshul Paigwar, Khushdeep Singh Mann, Alessandro Renzaglia, Christian Laugier
The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving.
1 code implementation • International Conference on Advanced Robotics (ICAR) 2021 • Unmesh Patil, Alessandro Renzaglia, Anshul Paigwar, Christian Laugier
Estimating the risk of collision with other road users is one of the most important modules to ensure safety in autonomous driving scenarios.
2 code implementations • 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021 • Anshul Paigwar, David Sierra-Gonzalez, Özgür Erkent, Christian Laugier
We train our network on the KITTI dataset and perform experiments to show the effectiveness of our network.
Ranked #1 on Object Localization on KITTI Pedestrian Easy
1 code implementation • 15 Nov 2020 • Anshul Paigwar, Özgür Erkent, David Sierra González, Christian Laugier
Ground plane estimation and ground point seg-mentation is a crucial precursor for many applications in robotics and intelligent vehicles like navigable space detection and occupancy grid generation, 3D object detection, point cloud matching for localization and registration for mapping.
1 code implementation • 14 Jun 2019 • Anshul Paigwar, Özgür Erkent, Christian Wolf, Christian Laugier
In this study, we propose Attentional Point- Net, which is a novel end-to-end trainable deep architecture for object detection in point clouds.